Guessing, if there is a Heart Attack or not

Statistical Methods and Laboratory, Part II - The Final Project

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Introduction

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Nowadays, with the improvement of the technologies there are an amount of data that allows us to understand if there is any problem or not in the healthy of a person.

Here in this project we explain and we try to predict whether a patient should be diagnosed with Heart Disease or not.

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The Dataset

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Kaggle, is the main platform where I found this interesting dataset where applying our main bayesian inference as the main scope of this project.

This dataset is composed by these features:

## 
## -- Column specification --------------------------------------------------------
## cols(
##   age = col_double(),
##   sex = col_double(),
##   cp = col_double(),
##   trtbps = col_double(),
##   chol = col_double(),
##   fbs = col_double(),
##   restecg = col_double(),
##   thalachh = col_double(),
##   exng = col_double(),
##   oldpeak = col_double(),
##   slp = col_double(),
##   caa = col_double(),
##   thall = col_double(),
##   output = col_double()
## )

## # A tibble: 6 x 14
##     age   sex    cp trtbps  chol   fbs restecg thalachh  exng oldpeak   slp
##   <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>   <dbl>    <dbl> <dbl>   <dbl> <dbl>
## 1    63     1     3    145   233     1       0      150     0     2.3     0
## 2    37     1     2    130   250     0       1      187     0     3.5     0
## 3    41     0     1    130   204     0       0      172     0     1.4     2
## 4    56     1     1    120   236     0       1      178     0     0.8     2
## 5    57     0     0    120   354     0       1      163     1     0.6     2
## 6    57     1     0    140   192     0       1      148     0     0.4     1
## # ... with 3 more variables: caa <dbl>, thall <dbl>, output <dbl>

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Analyzing these features, I recognized that the:

1. Age -> it refers to the age of the patient
2. sex -> it refers if the patient is:
   • male (1)
   • female (0)
3. cp -> it refers to the chest pain type that could be of four types:
   • 0 is the typical angina
   • 1 is the atypical angina
   • 2 is the non-anginal pain
   • 3 is the asymptomatic type
4. trestbps -> it refers to the resting blood pressure
5. chol -> it refers to the serum cholesterol in mg/dl
6. fbs -> it refers to the fasting blood sugar that if it is larger than 120 mg/dl is represented with:
   • 1 (true)
   • 0 (false)
7. restecg -> it refers to the resting electrocardiographic results:
   • 0 as Normal
   • 1 having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
   • 2. showing probable or definite left ventricular hypertrophy by Estes’ criteria
8. thalachh -> it refers to the maximum heart rate achieved
9. exng -> it refers to the exercise induced angina that could be:
   • 1 (yes)
   • 0 (no)
10. oldpeak -> it refers to the Previous peak
11. slp -> it refers to the peak exercise ST segment:
    • 0 as up sloping
    • 1 as flat
    • 2 as down sloping
12. caa -> it refers to the number of major vessels that goes from 0 to 3
13. thall -> it refers to the maximum heart rate achieved:
    • 0 as no-data
    • 1 as normal
    • 2 as fixed defect
    • 3 as reversible defect
14. output -> it refers to the fact to have the heart attack or not

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The main features detected are:

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##       age             sex               cp             trtbps     
##  Min.   :29.00   Min.   :0.0000   Min.   :0.0000   Min.   : 94.0  
##  1st Qu.:48.00   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:120.0  
##  Median :55.50   Median :1.0000   Median :1.0000   Median :130.0  
##  Mean   :54.42   Mean   :0.6821   Mean   :0.9636   Mean   :131.6  
##  3rd Qu.:61.00   3rd Qu.:1.0000   3rd Qu.:2.0000   3rd Qu.:140.0  
##  Max.   :77.00   Max.   :1.0000   Max.   :3.0000   Max.   :200.0  
##       chol            fbs           restecg          thalachh    
##  Min.   :126.0   Min.   :0.000   Min.   :0.0000   Min.   : 71.0  
##  1st Qu.:211.0   1st Qu.:0.000   1st Qu.:0.0000   1st Qu.:133.2  
##  Median :240.5   Median :0.000   Median :1.0000   Median :152.5  
##  Mean   :246.5   Mean   :0.149   Mean   :0.5265   Mean   :149.6  
##  3rd Qu.:274.8   3rd Qu.:0.000   3rd Qu.:1.0000   3rd Qu.:166.0  
##  Max.   :564.0   Max.   :1.000   Max.   :2.0000   Max.   :202.0  
##       exng           oldpeak           slp             caa        
##  Min.   :0.0000   Min.   :0.000   Min.   :0.000   Min.   :0.0000  
##  1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:1.000   1st Qu.:0.0000  
##  Median :0.0000   Median :0.800   Median :1.000   Median :0.0000  
##  Mean   :0.3278   Mean   :1.043   Mean   :1.397   Mean   :0.7185  
##  3rd Qu.:1.0000   3rd Qu.:1.600   3rd Qu.:2.000   3rd Qu.:1.0000  
##  Max.   :1.0000   Max.   :6.200   Max.   :2.000   Max.   :4.0000  
##      thall           output     
##  Min.   :0.000   Min.   :0.000  
##  1st Qu.:2.000   1st Qu.:0.000  
##  Median :2.000   Median :1.000  
##  Mean   :2.315   Mean   :0.543  
##  3rd Qu.:3.000   3rd Qu.:1.000  
##  Max.   :3.000   Max.   :1.000

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The Data Visualization

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In this subsection we want to describe graphically the dataset that we are analyzing to have a first taste of what is the better model to use in this case, we want to show below some interesting plots

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The visualization using PCA

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PCA is a particular tool that allows us to show the behaviour of the patients (treated as points in this space) of our data. We see in this way which are the patients more similar to each other

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Its normal to denote that there could be some losses on this representation due to the amount of features that we want to consider.

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The Categorical Data

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Here, we want to analyze the percentages of each categorical data:

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The Quantitative Data

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Here, we illustrate the main features of the quantitative data, after have seen the categorical data in the previous section:

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hist.and.summary('age', 'Persons Age')

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   29.00   48.00   55.50   54.42   61.00   77.00

hist.and.summary('chol', 'Cholestoral')

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   126.0   211.0   240.5   246.5   274.8   564.0

hist.and.summary('thalachh', 'Maximum Heart Rate Achieved')

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    71.0   133.2   152.5   149.6   166.0   202.0

hist.and.summary('trtbps', 'Resting Blood Pressure')

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    94.0   120.0   130.0   131.6   140.0   200.0

hist.and.summary('oldpeak', 'Previous Peak')

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   0.000   0.800   1.043   1.600   6.200

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After this, we consider also the densities considering the case of having the heart attack and not:

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dense.chart('age', 'Persons Age')

dense.chart('chol', 'Cholestoral')

dense.chart('thalachh', 'Maximum Heart Rate Achieved')

dense.chart('trtbps', 'Resting Blood Pressure')

dense.chart('oldpeak', 'Previous Peak')

The Correlations

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Here, we want to highlight the correlations between the features treated in qualitative, quantitative and without any distinction.

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The Goal

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The main goal is to leverage the main fully Bayesian analysis, based on understanding if a person has a heart attack or not.

So, the response variable \(Y_i\) (that is the “output” feature in our dataset) is the heart attack \(\in \{0,1\}\) and the predictor variables (\(x_i \in \mathbb{R}^{+}\)) have been chosen using the glm() function, used to fit generalized linear models, as we can see below:

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## 
## Call:
## glm(formula = y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + 
##     x10 + x11 + x12 + x13, family = binomial(), data = dat)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5751  -0.3868   0.1514   0.5841   2.6239  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  3.304169   2.577891   1.282 0.199936    
## x1          -0.001469   0.023464  -0.063 0.950062    
## x2          -1.750930   0.468199  -3.740 0.000184 ***
## x3           0.847283   0.185548   4.566 4.96e-06 ***
## x4          -0.020188   0.010386  -1.944 0.051916 .  
## x5          -0.004489   0.003806  -1.179 0.238252    
## x6           0.073463   0.532452   0.138 0.890263    
## x7           0.450607   0.348505   1.293 0.196021    
## x8           0.023134   0.010449   2.214 0.026835 *  
## x9          -0.981017   0.409806  -2.394 0.016672 *  
## x10         -0.523604   0.214467  -2.441 0.014630 *  
## x11          0.589074   0.349864   1.684 0.092236 .  
## x12         -0.826015   0.201922  -4.091 4.30e-05 ***
## x13         -0.887203   0.290730  -3.052 0.002276 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 416.42  on 301  degrees of freedom
## Residual deviance: 210.35  on 288  degrees of freedom
## AIC: 238.35
## 
## Number of Fisher Scoring iterations: 6

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As we can see above, the glm rejects the hypothesis to consider the variables:

• Age ~ \(x_1\)
• Cholestoral ~ \(x_5\)
• fasting blood sugar ~ \(x_6\)
• resting electrocardiographic results ~ \(x_7\)

… and also the intercept isn’t a good choice to admit it in our model.

then, in summary we have N = 302 observations. Then we decided to consider two models and see the difference on which model at the end is the best model of our analysis.

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Preliminary brief definitions

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• Likelihood \(\pi(y_{obs}|\theta)\): measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters. It is formed from the joint probability distribution of the sample, but viewed and used as a function of the parameters only, thus treating the random variables as fixed at the observed values.

• Logistic regression model: Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.

• logit link function: it uses the Cumulative Distribution Function (CDF) of the logistic distribution. A benefit to using Logit is that the coefficients can be interpreted in the terms of odds ratios.

• cloglog function: Is unlike Logit and Probit asymmetric. It is best used when the probability of an event is very small or very large. The complementary log-log approaches 0 infinitely slower than any other link function. Cloglog model is closely related to continuous-time models for the occurrence of events, so it has an important application in the area of survival analysis and hazard modeling.

• Prior \(\pi(\theta)\): is the probability distribution that would express one’s beliefs about this quantity before some evidence is taken into account.

• the deviance: is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis testing. It is a generalization of the idea of using the sum of squares of residuals (RSS) in ordinary least squares to cases where model-fitting is achieved by maximum likelihood.

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The First Model (link function with logit)

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In the first model, we decide to consider the following logistic regression model with the logit link function:

\[ Y_i \sim Bern(p_i) \\ logit(p_i) = \beta_{2} \cdot x_{2_i} + \beta_{3} \cdot x_{3_i} + \beta_{4} \cdot x_{4_i} +\beta_{8} \cdot x_{8_i} + \beta_{9} \cdot x_{9_i} + \beta_{10} \cdot x_{10_i} + \beta_{11} \cdot x_{11_i} + \beta_{12} \cdot x_{12_i} + \beta_{13} \cdot x_{13_i} \] The prior beta parameters are chosen approximatly considering the estimated averages saw in the previous subsection regarding the outcomes of glm(). So, the beta prior parameters are distributed following a normal distribution as follows:

\[ \beta_2 \sim N(\mu_2 = -1, \tau^{2} = 1.0 \cdot 10^{-6}) \\ \beta_3 \sim N(\mu_3 = 0.5, \tau^{2} = 1.0 \cdot 10^{-6}) \\ \beta_4 \sim N(\mu_4 = 0, \tau^{2} = 1.0 \cdot 10^{-6}) \\ \beta_8 \sim N(\mu_8 = 0, \tau^{2} = 1.0 \cdot 10^{-6}) \\ \beta_9 \sim N(\mu_9 = -0.5, \tau^{2} = 1.0 \cdot 10^{-6}) \\ \beta_{10} \sim N(\mu_{10} = 0.5, \tau^{2} = 1.0 \cdot 10^{-6}) \\ \beta_{11} \sim N(\mu_{11} = 0.5, \tau^{2} = 1.0 \cdot 10^{-6}) \\ \beta_{12} \sim N(\mu_{12} = -0.5, \tau^{2} = 1.0 \cdot 10^{-6}) \\ \beta_{13} \sim N(\mu_{13} = -0.5, \tau^{2} = 1.0 \cdot 10^{-6}) \\ \]

The main reason is that we prefer chosing the binary classification as in this case the logistic regression to model our outcomes based only on 0 or 1.

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Implementing RJags

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As we can see, we implemented the model using RJags:

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• The model is defined as follows:

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model <- function(){
  # Likelihood
  for (i in 1:N){
    y[i] ~ dbern(p[i])
    logit(p[i]) <-  beta2*x2[i] + beta3*x3[i] + beta4*x4[i] + beta8*x8[i] + beta9*x9[i] + beta10*x10[i] + beta11*x11[i] + beta12*x12[i] + beta13*x13[i] # omit intercept and not useful variables
  }
  
  # Defining the prior beta parameters
  beta2 ~ dnorm(-1, 1.0E-6)
  beta3 ~ dnorm(0.5, 1.0E-6)
  beta4 ~ dnorm(0, 1.0E-6)
  beta8 ~ dnorm(0, 1.0E-6)
  beta9 ~ dnorm(-0.5, 1.0E-6)
  beta10 ~ dnorm(0.5, 1.0E-6)
  beta11 ~ dnorm(0.5, 1.0E-6)
  beta12 ~ dnorm(-0.5, 1.0E-6)
  beta13 ~ dnorm(-0.5, 1.0E-6)
}

remark: that the standard deviation in RJags is considered as the precision, so the \(\tau^2 = \frac{1}{\sigma^{2}}\)

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• Data structuring as requested with highlight which parameters to consider:

# Passing the data for RJags
data.jags <- list("y" = y, "N" = N,
                  "x2" = x2, "x3" = x3, "x4" = x4, "x8" = x8, "x9" = x9, "x10" = x10, "x11" = x11, "x12" = x12, "x13" = x13)

# Defining parameters of interest to show after running RJags
mod.params <- c("beta2", "beta3", "beta4", "beta8", "beta9", "beta10", "beta11", "beta12", "beta13")

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• Running the model with the following results:

# Run JAGS
set.seed(123)
n.chains <- 3
mod.fit <- jags(data = data.jags,                                                 # DATA
                model.file = model,                                               # MODEL
                parameters.to.save = mod.params,                                  # TRACKING
                n.chains = n.chains, n.iter = 10000, n.burnin = 1000, n.thin=10)  # MCMC

## module glm loaded

## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 302
##    Unobserved stochastic nodes: 9
##    Total graph size: 3840
## 
## Initializing model

mod.fit

## Inference for Bugs model at "C:/Users/Jeremy/AppData/Local/Temp/RtmpknYfOZ/model563036061b32.txt", fit using jags,
##  3 chains, each with 10000 iterations (first 1000 discarded), n.thin = 10
##  n.sims = 2700 iterations saved
##          mu.vect sd.vect    2.5%     25%     50%     75%   97.5%  Rhat n.eff
## beta10    -0.489   0.212  -0.909  -0.628  -0.490  -0.344  -0.081 1.001  2700
## beta11     0.683   0.344   0.021   0.440   0.687   0.909   1.369 1.001  2700
## beta12    -0.846   0.199  -1.252  -0.977  -0.843  -0.711  -0.471 1.002  1900
## beta13    -0.851   0.277  -1.401  -1.038  -0.842  -0.659  -0.332 1.001  2700
## beta2     -1.652   0.445  -2.530  -1.955  -1.640  -1.356  -0.810 1.001  2700
## beta3      0.881   0.187   0.524   0.756   0.879   1.006   1.253 1.002  2700
## beta4     -0.015   0.008  -0.032  -0.020  -0.015  -0.009   0.001 1.002  1700
## beta8      0.031   0.008   0.016   0.026   0.031   0.037   0.047 1.002  1600
## beta9     -0.889   0.414  -1.688  -1.173  -0.895  -0.605  -0.069 1.001  2700
## deviance 225.361   4.463 218.784 222.067 224.697 227.789 235.802 1.001  2700
## 
## For each parameter, n.eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
## 
## DIC info (using the rule, pD = var(deviance)/2)
## pD = 10.0 and DIC = 235.3
## DIC is an estimate of expected predictive error (lower deviance is better).

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In this last running of the MCMC we have interesting results:

• mu.vect and sd.vect: these values representing the estimated values after simulating the MCMC.

• the percentages (quantiles): represents the quantiles of the interval where the parameter is likely in

• RHat: The values near 1 suggest convergence

• n.eff: is the effective number of samples to achieve the convergence, the stationary region.

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The stationary regions

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Here, we show the univariate trace-plots of the simulations of each parameter:

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The Cumulative Means

\[~\] In this section we want to see the empirical average of \(\mathbf{\hat{I}}_{t}\) increasing the value of \(t \, = \, 1,...,T\). Before starting, we want to write the formula of \(\mathbf{\hat{I}}_{t}\) that is:

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\[ \mathbf{I} \approx \mathbf{\hat{I}}_{t} = \frac{1}{T} \sum_{i=1}^{T} h(\theta^{(i)}) \]

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is important to write \(\approx\) because this leverages the SLLN theorem and we want also to confirm this assumption! So, let’s see the results:

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Some other interesting plots

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We are considering other interesting plots, underlining also each chain alone:

\[~\] Now, we want to see the autocorrelations of each parameter:

##               beta10       beta11       beta12       beta13       beta2
## Lag 0    1.000000000  1.000000000  1.000000000  1.000000000 1.000000000
## Lag 10   0.028749715  0.013027320  0.011401177  0.008061257 0.009605275
## Lag 50   0.004853036  0.007741027  0.006044363 -0.018532707 0.032820945
## Lag 100 -0.062010722 -0.017318088  0.005858652  0.022871691 0.044325748
## Lag 500  0.022963511  0.034443073 -0.006998113  0.004928576 0.002334635
##              beta3        beta4       beta8        beta9      deviance
## Lag 0   1.00000000  1.000000000  1.00000000  1.000000000  1.000000e+00
## Lag 10  0.02486498  0.001619878  0.04591646 -0.004177052 -1.524460e-02
## Lag 50  0.02580283 -0.048039671 -0.04290369 -0.023810367 -9.722008e-03
## Lag 100 0.02351930 -0.011874447 -0.01503457 -0.028663890 -9.939696e-05
## Lag 500 0.03144189  0.021556527  0.01383545  0.018783666  1.420195e-02

\[~\] The ACFs follow good behaviours, because we need to have samples independent each other during new iterations. Here, we see that the correlations are going further from the first lag, this is strictly decreasing. So, this is a good point!

We can conferm our intuition also with the values returned by autocorr.diag() that it returned the vector of the average autocorrelations across all chains.

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The Approximation Errors

\[~\] Here, we want to analyze the best approximation error for the MCMC sampling. We consider, essentially the square root of the MCSE.

The variance formula in the MCMC samplig is:

\[ \mathbf{V}[\hat{I}_{t}] = \frac{Var_{\pi}[h(X_{1})]}{T_{eff}} = \Big( 1 + 2 \sum_{k=1}^{\infty} \rho_{k}\Big)\frac{\sigma^{2}}{T} \] in this case we want to consider the MCSE that is the square root of the formula written above, So the results are (for each chain):

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n <- length(colnames(mod.fit$BUGSoutput$sims.matrix))
mcse_dataframe <- data.frame(MCSE_Chain1 = rep(NA, n), MCSE_Chain2 = rep(NA, n), MCSE_Chain3 = rep(NA, n), MCSE_mean = rep(NA, n))
rownames(mcse_dataframe) <- colnames(mod.fit$BUGSoutput$sims.matrix)[1:n] 

for(colname in colnames(mod.fit$BUGSoutput$sims.matrix)[1:n]){
  for(j in 1:n.chains)
    mcse_dataframe[colname, j] <- LaplacesDemon::MCSE(mod.fit$BUGSoutput$sims.array[, j, colname])
  mcse_dataframe[colname, "MCSE_mean"] <- mean(unlist(mcse_dataframe[colname, c(1:3)]))
}

mcse_dataframe

##           MCSE_Chain1  MCSE_Chain2  MCSE_Chain3    MCSE_mean
## beta10   0.0073261667 0.0079692464 0.0077954121 0.0076969417
## beta11   0.0115131110 0.0118303126 0.0120922370 0.0118118869
## beta12   0.0065660674 0.0073391536 0.0064715431 0.0067922547
## beta13   0.0100578467 0.0090567755 0.0094117785 0.0095088003
## beta2    0.0145511257 0.0141811879 0.0177755410 0.0155026182
## beta3    0.0070692107 0.0063147178 0.0072267372 0.0068702219
## beta4    0.0002681413 0.0003043189 0.0002726522 0.0002817041
## beta8    0.0002727964 0.0003043320 0.0002715205 0.0002828829
## beta9    0.0129126027 0.0146421457 0.0146747039 0.0140764841
## deviance 0.1550249861 0.1362655438 0.1552283978 0.1488396426

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Hypotheses and Testing of the convergences of these multiple Markov chains

\[~\] Here, we want to understand if we achieve with these multiple simulations of the markov chains the convergences. We are considering one of the tools saw in the last lectures, to understand if we achieve or not the convergency this is more powerful than the other tools.

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Heidel Test

\[~\] Implements a convergence diagnostic, and removes up to half the chain in order to ensure that the means are estimated from a chain that has converged.

The convergence test uses the Cramer-von-Mises statistic to test the null hypothesis that the sampled values come from a stationary distribution.

The test is successively applied, firstly to the whole chain, then after discarding the first 10%, 20%, … of the chain until either the null hypothesis is accepted, or 50% of the chain has been discarded.

The latter outcome constitutes `failure’ of the stationarity test and indicates that a longer MCMC run is needed. If the stationarity test is passed, the number of iterations to keep and the number to discard are reported.

The half-width test calculates a 95% confidence interval for the mean, using the portion of the chain which passed the stationarity test.

Half the width of this interval is compared with the estimate of the mean. If the ratio between the half-width and the mean is lower than eps, the halfwidth test is passed. Otherwise the length of the sample is deemed not long enough to estimate the mean with sufficient accuracy.

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## [[1]]
##                                        
##          Stationarity start     p-value
##          test         iteration        
## beta10   passed       1         0.3239 
## beta11   passed       1         0.6880 
## beta12   passed       1         0.2287 
## beta13   passed       1         0.7323 
## beta2    passed       1         0.7564 
## beta3    passed       1         0.7176 
## beta4    passed       1         0.0622 
## beta8    passed       1         0.2670 
## beta9    passed       1         0.6451 
## deviance passed       1         0.1120 
##                                      
##          Halfwidth Mean     Halfwidth
##          test                        
## beta10   passed     -0.4835 0.014111 
## beta11   passed      0.6757 0.020904 
## beta12   passed     -0.8545 0.012844 
## beta13   passed     -0.8541 0.017958 
## beta2    passed     -1.6552 0.029211 
## beta3    passed      0.8795 0.012322 
## beta4    passed     -0.0147 0.000559 
## beta8    passed      0.0316 0.000491 
## beta9    passed     -0.8927 0.025430 
## deviance passed    225.2703 0.297930 
## 
## [[2]]
##                                        
##          Stationarity start     p-value
##          test         iteration        
## beta10   passed       1         0.233  
## beta11   passed       1         0.254  
## beta12   passed       1         0.855  
## beta13   passed       1         0.784  
## beta2    passed       1         0.699  
## beta3    passed       1         0.153  
## beta4    passed       1         0.642  
## beta8    passed       1         0.812  
## beta9    passed       1         0.423  
## deviance passed       1         0.769  
##                                      
##          Halfwidth Mean     Halfwidth
##          test                        
## beta10   passed     -0.4928 0.013672 
## beta11   passed      0.6839 0.022893 
## beta12   passed     -0.8391 0.013463 
## beta13   passed     -0.8527 0.016625 
## beta2    passed     -1.6389 0.028610 
## beta3    passed      0.8842 0.011386 
## beta4    passed     -0.0149 0.000536 
## beta8    passed      0.0315 0.000566 
## beta9    passed     -0.8891 0.027043 
## deviance passed    225.3264 0.268161 
## 
## [[3]]
##                                        
##          Stationarity start     p-value
##          test         iteration        
## beta10   passed       1         0.3448 
## beta11   passed       1         0.1388 
## beta12   passed       1         0.6863 
## beta13   passed       1         0.1368 
## beta2    passed       1         0.0714 
## beta3    passed       1         0.3191 
## beta4    passed       1         0.5506 
## beta8    passed       1         0.8238 
## beta9    passed       1         0.8632 
## deviance passed       1         0.5795 
##                                      
##          Halfwidth Mean     Halfwidth
##          test                        
## beta10   passed     -0.4906 0.013692 
## beta11   passed      0.6899 0.022293 
## beta12   passed     -0.8431 0.012653 
## beta13   passed     -0.8475 0.018361 
## beta2    passed     -1.6604 0.031817 
## beta3    passed      0.8795 0.014677 
## beta4    passed     -0.0142 0.000546 
## beta8    passed      0.0310 0.000524 
## beta9    passed     -0.8847 0.031369 
## deviance passed    225.4861 0.294261

\[~\] We prefer considering the Heidel test because it is a double check of the stationarity and the convergency states. This is sometimes failed, meanwhile the other tools seems to be good, so I prefer considering this test.

Here, we can see all is considered correctly, so our intuitions are correct!

\[~\]

The Predictions

\[~\]

Here, we want to illustrate how using the markov chain to approximate the posterior predictive distribution of the people that could have the heart attack or not considering the features explained above. So, we want to approximate this area:

\[ m(y_{new}) = \int_{\Theta} f(y_{new}|\theta, Y_{old}, x_2, x_3, x_4, x_8, x_9, x_{10}, x_{11}, x_{12}, x_{13})\pi(\theta)d\theta \]

where the \(\theta\) is equal to the coefficients used and considered before, \(\Theta\) is the parameter space and \(\pi(\theta)\) is the product of the priors thanks to marginal independence assumption. At the end, we want to smaple from the \(\pi(\theta)\) and from \(f(y_{new}|\theta, Y_{old}, x_2, x_3, x_4, x_8, x_9, x_{10}, x_{11}, x_{12}, x_{13})\).

Here, there is a brief practical example:

new_record <- list(x2 = sample(x2, 1, replace = T), x3 = sample(x3, 1, replace = T), x4 = sample(x4, 1, replace = T), x8 = sample(x8, 1, replace = T), x9 = sample(x9, 1, replace = T),
                   x10 = sample(x10, 1, replace = T), x11 = sample(x11, 1, replace = T), x12 = sample(x12, 1, replace = T), x13 = sample(x13, 1, replace = T))

x <- mod.fit$BUGSoutput$sims.matrix[,"beta2"]*new_record$x2 + mod.fit$BUGSoutput$sims.matrix[,"beta3"]*new_record$x3 + mod.fit$BUGSoutput$sims.matrix[,"beta4"]*new_record$x4 + mod.fit$BUGSoutput$sims.matrix[,"beta8"]*new_record$x8 + mod.fit$BUGSoutput$sims.matrix[,"beta9"]*new_record$x9 + mod.fit$BUGSoutput$sims.matrix[,"beta10"]*new_record$x10 + mod.fit$BUGSoutput$sims.matrix[,"beta11"]*new_record$x11 + mod.fit$BUGSoutput$sims.matrix[,"beta12"]*new_record$x12 + mod.fit$BUGSoutput$sims.matrix[,"beta13"]*new_record$x13 
x_mu <- 1/(1+exp(-x))

y_pred <- unlist(lapply(x_mu, function(x) rbinom(n = 1, size = 1, prob = x)))
sd(y_pred)

## [1] 0.3478183

prop.table(table(y_pred))

## y_pred
##         0         1 
## 0.8592593 0.1407407

prop.table(table(y))

## y
##         0         1 
## 0.4569536 0.5430464

summary(y_pred)[4]

##      Mean 
## 0.1407407

hchart(y_pred, type = "column", name = "model1", color = randomcoloR::randomColor()) %>%
  hc_title(text = "Predictions with model 1") %>%
  hc_xAxis(title = "model1") %>%
  hc_chart(options3d=list(enabled=TRUE, alpha=2, beta=-10, 
                          depth=100, viewDistance=25)) %>% 
  hc_plotOptions(column=list(depth= 100))

After read, the whole project report we will see which is the best model here in terms of predictions, stay tuned!

\[~\]

The Correlations

\[~\]

Here, we bring the correlations between all the values calculated during the MCMC sampling, considering the joining matrix:

\[~\]

The Second Model

\[~\] Now, we want to focus to another model, that is at the same time the logistic regression, but here we changed the link function, now has you can see in the previous chapters is the cloglog function.

Why this changment? Because, we want to see which model is better… and how can you see this? I’ll tell you where you can easy see this better preference.

What is the link function? So, the link function transforms the probabilities of the levels of a categorical response variable to a continuous scale that is unbounded. Once the transformation is complete, the relationship between the predictors and the response can be modeled with the logistic regression for example.

As we can see we will reproduce this second model changing the link function to see if it is better or not than the previous model:

\[~\]

## module glm loaded

## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 302
##    Unobserved stochastic nodes: 9
##    Total graph size: 3840
## 
## Initializing model

## Inference for Bugs model at "C:/Users/Jeremy/AppData/Local/Temp/Rtmp42sAgQ/model62a433132292.txt", fit using jags,
##  3 chains, each with 10000 iterations (first 1000 discarded), n.thin = 10
##  n.sims = 2700 iterations saved
##          mu.vect sd.vect    2.5%     25%     50%     75%   97.5%  Rhat n.eff
## beta10    -0.494   0.211  -0.930  -0.629  -0.489  -0.356  -0.077 1.000  2700
## beta11     0.682   0.344  -0.008   0.453   0.682   0.909   1.343 1.001  2700
## beta12    -0.852   0.202  -1.255  -0.988  -0.849  -0.714  -0.464 1.000  2700
## beta13    -0.846   0.270  -1.390  -1.026  -0.843  -0.663  -0.335 1.001  2700
## beta2     -1.643   0.443  -2.538  -1.931  -1.622  -1.340  -0.807 1.001  2700
## beta3      0.873   0.190   0.508   0.743   0.875   0.999   1.258 1.003  2600
## beta4     -0.014   0.008  -0.031  -0.020  -0.014  -0.009   0.001 1.001  2500
## beta8      0.031   0.008   0.016   0.026   0.031   0.036   0.047 1.001  2700
## beta9     -0.882   0.414  -1.679  -1.161  -0.883  -0.603  -0.090 1.001  2500
## deviance 225.326   4.559 218.528 222.066 224.650 227.855 236.465 1.004  1000
## 
## For each parameter, n.eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
## 
## DIC info (using the rule, pD = var(deviance)/2)
## pD = 10.4 and DIC = 235.7
## DIC is an estimate of expected predictive error (lower deviance is better).

\[~\] Can you see something different? Seems yes.. see the DIC! Is better the previous model (although the difference is too tiny, but the previous model is better).

…mmh, what is the DIC?

\[~\]

The comparison between these two models

\[~\] As we can see above, we tried to make a different model only changing the link function to see if it is better to associate with our data, the DIC (Deviance information criterion) is our indicator that says to us which is the better model.

The deviance information criterion (DIC) is a hierarchical modeling generalization of the Akaike information criterion (AIC). It is particularly useful in Bayesian model selection problems where the posterior distributions of the models have been obtained by Markov chain Monte Carlo (MCMC) simulation. DIC is an asymptotic approximation as the sample size becomes large, like AIC.

the DIC is calculated as:

\[ DIC = p_D + \overline{D(\theta)} \] where:

• there are two calculations in common usage for the effective number of parameters of the model.
  • the first, as described in Spiegalhalter et al. (2002, p.587) is p_D = \(\overline{D(\theta)} - D(\overline{\theta})\), where \(\overline{\theta}\) is the expectation of \(\theta\)
  • …and the second described in Gelman et al. (2004, p.587) is \(p_D = \frac{1}{2}\mathbb{V}(D(\theta))\).

The larger the effective number of parameters is, the easier it is for the model to fit the data, and so the deviance needs to be penalized.

• the deviance is \(D(\theta) = -2log(p(y|\theta)) + C\), where y are the data, \(\theta\) are unknown parameters of the model and C is a constant.

Lower is the DIC value better is the accuracy of the model, in this case is better the first model.

\[~\]

The Conclusions

\[~\]

\[~\]

The References

\[~\]